Submitted to: The Plant Phenome Journal
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/17/2025
Publication Date: 5/21/2025
Citation: Newman, C., Austin, R., Andres, R., Read, Q.D., Garrity, N., Fritz, K., Oakley, A., Hulse-Kemp, A.M., Dunne, J. 2025. Evaluating UAV-captured RGB and multispectral imagery as a proxy for visual rating of leaf spot in cultivated peanut . The Plant Phenome Journal. https://doi.org/10.1002/ppj2.70019.
DOI: https://doi.org/10.1002/ppj2.70019

Interpretive Summary: Breeding for disease resistance is very tricky, it relies on the disease to be present across materials to be evaluated as well as the breeders to be able to accurately tell the level of resistance an individual line is experiencing. The work for the breeder is complicated further when multiple visual symptoms may be occurring that are hard to decipher. There is need to develop tools that will help the breeder obtain more accurate data faster to make better decisions to get better materials to growers. This study developed a new tool utilizing drones to help the breeder be able to evaluate disease resistance for an important disease in peanut that causes significant yield losses. This new tool potentially supports more sustainable peanut production reducing the need for chemical controls.

Technical Abstract: Leaf spot is a devastating disease in cultivated peanut that can lead to significant yield losses without chemical controls. Multiple disease symptoms, two causal organisms, inconsistent testing environments, and genotype by environment interactions are all components which make breeding for leaf spot resistant peanuts challenging. To better understand this disease, and make gains in breeding for disease resistance, an accurate and effective phenotyping strategy must be implemented. In this work, data derived from leaf scans and UAV-captured RGB and multi-spectral imagery were evaluated as a replacement for the subjective visual rating scale used at present. Standard operating procedures are detailed for all digital methods evaluated in this paper, and all digital phenotypes are fully characterized with descriptive statistics. Feature importance and post hoc proof of concept studies are conducted to further evaluate the new digital methods. Ultimately, ‘Visible Atmospherically Resistant Index’ is selected as the most appropriate proxy for immediate use by researchers and plant breeders in the peanut community.